Discovering computer science: interdisciplinary problems, principles, and python programming

By:

Havill, Jessen

Material type: Text

TextPublication details: CRC Press Boco Raton 2021Edition: 2ndDescription: 516 pISBN:

9780367472498

Subject(s):

DDC classification:

005.133 HAV

Summary: Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming　is a problem-oriented introduction to computational problem solving and programming in Python, appropriate for a first course for computer science majors, a more targeted disciplinary computing course or, at a slower pace, any introductory computer science course for a general audience. Realizing that an organization around language features only resonates with a narrow audience, this textbook instead connects programming to students’ prior interests using a range of authentic problems from the natural and social sciences and the digital humanities. The presentation begins with an introduction to the problem-solving process, contextualizing programming as an essential component. Then, as the book progresses, each chapter guides students through solutions to increasingly complex problems, using a spiral approach to introduce Python language features. The text also places programming in the context of fundamental computer science principles, such as abstraction, efficiency, testing, and algorithmic techniques, offering glimpses of topics that are traditionally put off until later courses. This book contains 30 well-developed independent projects that encourage students to explore questions across disciplinary boundaries, over 750 homework exercises, and 300 integrated reflection questions engage students in problem solving and active reading.

List(s) this item appears in: IT & Decision Sciences | Fiction

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	005.133 HAV (Browse shelf(Opens below))	1	Available		004214

Table of Contents
Preface

Acknowledgments

About the author

How to Solve It

UNDERSTAND THE PROBLEM

DESIGN AN ALGORITHM

WRITE A PROGRAM

LOOK BACK

SUMMARY AND FURTHER DISCOVERY

Visualizing Abstraction

DATA ABSTRACTION

DRAWING FLOWERS AND PLOTTING EARTHQUAKES

FUNCTIONAL ABSTRACTION

PROGRAMMING IN STYLE

A RETURN TO FUNCTIONS

SCOPE AND NAMESPACES

SUMMARY AND FURTHER DISCOVERY

Inside a Computer

COMPUTERS ARE DUMB

EVERYTHING IS BITS

COMPUTER ARITHMETIC

BINARY ARITHMETIC

THE UNIVERSAL MACHINE

Growth and Decay

ACCUMULATORS

DATA VISUALIZATION

CONDITIONAL ITERATION

CONTINUOUS MODELS

NUMERICAL ANALYSIS

SUMMING UP

FURTHER DISCOVERY

PROJECTS

Forks in the Road
RANDOM WALKS
PSEUDORANDOM NUMBER GENERATORS

SIMULATING PROBABILITY DISTRIBUTIONS

BACK TO BOOLEANS

DEFENSIVE PROGRAMMING

GUESS MY NUMBER

SUMMARY AND FURTHER DISCOVERY

PROJECTS

Text, Documents, and DNA
FIRST STEPS

TEXT DOCUMENTS

A CONCORDANCE

WORD FREQUENCY TRENDS

COMPARING TEXTS

TIME COMPLEXITY

COMPUTATIONAL GENOMICS

SUMMARY AND FURTHER DISCOVERY

PROJECTS

Data Analysis
SUMMARY STATISTICS

WRANGLING DATA

TALLYING FREQUENCIES

READING TABULAR DATA

DESIGNING EFFICIENT ALGORITHMS

LINEAR REGRESSION

DATA CLUSTERING

SUMMARY AND FURTHER DISCOVERY

PROJECTS

Flatland
TABULAR DATA

THE GAME OF LIFE

DIGITAL IMAGES

SUMMARY AND FURTHER DISCOVERY

PROJECTS

Self-similarity and Recursion

FRACTALS

RECURSION AND ITERATION

THE MYTHICAL TOWER OF HANOI

RECURSIVE LINEAR SEARCH

DIVIDE AND CONQUER

LINDENMAYER SYSTEMS

9SUMMARY AND FURTHER DISCOVERY

PROJECTS

Organizing Data
BINARY SEARCH

SELECTION SORT

INSERTION SORT

EFFICIENT SORTING

TRACTABLE AND INTRACTABLE ALGORITHMS

SUMMARY AND FURTHER DISCOVERY

PROJECTS

Networks
MODELING WITH GRAPHS

SHORTEST PATHS

IT’S A SMALL WORLD

RANDOM GRAPHS

SUMMARY AND FURTHER DISCOVERY

PROJECTS

Object-oriented Design
SIMULATING AN EPIDEMIC
OPERATORS AND POLYMORPHISM

Discovering Computer Science: Interdisciplinary Problems, Principles, and Python Programming　is a problem-oriented introduction to computational problem solving and programming in Python, appropriate for a first course for computer science majors, a more targeted disciplinary computing course or, at a slower pace, any introductory computer science course for a general audience.

Realizing that an organization around language features only resonates with a narrow audience, this textbook instead connects programming to students’ prior interests using a range of authentic problems from the natural and social sciences and the digital humanities. The presentation begins with an introduction to the problem-solving process, contextualizing programming as an essential component. Then, as the book progresses, each chapter guides students through solutions to increasingly complex problems, using a spiral approach to introduce Python language features.

The text also places programming in the context of fundamental computer science principles, such as abstraction, efficiency, testing, and algorithmic techniques, offering glimpses of topics that are traditionally put off until later courses.

This book contains 30 well-developed independent projects that encourage students to explore questions across disciplinary boundaries, over 750 homework exercises, and 300 integrated reflection questions engage students in problem solving and active reading.

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